Abstract
Mental stress can lead to traffic accidents by reducing a driver’s concentration or increasing fatigue while driving. In recent years, demand for methods to detect drivers’ stress in advance to prevent dangerous situations increased. Thus, we propose a novel method for detecting driving stress using nonlinear representations of short-term (30 s or less) physiological signals for multimodal convolutional neural networks (CNNs). Specifically, from hand/foot galvanic skin response (HGSR, FGSR) and heart rate (HR) short-term input signals, first, we generate corresponding two-dimensional nonlinear representations called continuous recurrence plots (Cont-RPs). Second, from the Cont-RPs, we use multimodal CNNs to automatically extract FGSR, HGSR, and HR signal representative features that can effectively differentiate between stressed and relaxed states. Lastly, we concatenate the three extracted features into one integrated representation vector, which we feed to a fully connected layer to perform classification. For the evaluation, we use a public stress dataset collected from actual driving environments. Experimental results show that the proposed method demonstrates superior performance for 30-s signals, with an overall accuracy of 95.67%, an approximately 2.5–3% improvement compared with that of previous works. Additionally, for 10-s signals, the proposed method achieves 92.33% classification accuracy, which is similar to or better than the performance of other methods using long-term signals (over 100 s).
Highlights
Excessive mental stress can negatively affect people in numerous ways, such as by causing various diseases or reducing concentration and work efficiency [1,2,3,4]
Unlike conventional studies that utilize statistical features or domain knowledge-based feature engineering, we explored the nonlinear features presented in continuous recurrence plot (RP) of short-term foot galvanic skin response (GSR) (FGSR), hand GSR (HGSR), and heart rate (HR) signals, along with multimodal convolutional neural networks (CNNs)
We proposed a new method for detecting drivers’ stress based on short-term physiological signals, namely, FGSR, HGSR, and HR, which can be obtained by wearable devices
Summary
Excessive mental stress can negatively affect people in numerous ways, such as by causing various diseases or reducing concentration and work efficiency [1,2,3,4]. Vehicle motion measurements mainly include drivers’ acceleration, braking, lane position, steering angle, and handle movement patterns [10,11,12]. Such measurements are obtainable but dependent on vehicle types, driving habits, or road conditions. Facial behavior measurements, such as eye gaze status, pupil dilation, blink rate, yawning, and head movement, can be acquired without interfering with the driver [13,14,15] These measurements tend to be unstable under certain conditions, such as poor lighting, bad weather, at night, or when a driver is wearing eyeglasses
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